#define RELU(x) fmax(x, 0.0f)

__constant sampler_t samplerN = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_NEAREST;

__constant float kernelsL1[9 * 8] = 
{
-2.0676e-02f,  6.7641e-03f,  2.8287e-01f,
 2.5576e-01f,  1.9765e-01f, -2.4700e-01f,
 3.5056e-01f,  2.9306e-01f, -2.2245e-01f,
 8.4706e-02f, -2.9455e-01f, -5.5831e-02f,
-8.4635e-02f, -9.6835e-02f,  3.1208e-01f,
 1.7690e-01f,  2.7624e-02f,  5.1954e-02f,
-5.3869e-01f,  7.2934e-02f, -1.7662e-03f,
-3.1402e-02f,  3.1700e-01f,  1.4965e-01f,
 3.8569e-02f,  5.5025e-03f, -6.6555e-03f,
-4.2049e-38f, -4.1971e-38f, -4.1488e-38f,
-4.2855e-38f, -4.2871e-38f, -4.2363e-38f,
-4.1861e-38f, -4.1974e-38f, -4.1677e-38f,
 1.8451e-01f, -5.4584e-02f,  1.4494e-01f,
 1.3433e-01f,  1.0073e-01f,  2.6371e-01f,
 6.1261e-02f,  2.2116e-01f,  2.0074e-01f,
 5.9669e-02f, -3.9168e-02f,  2.1674e-01f,
-2.9132e-01f,  3.0285e-03f,  1.2625e-01f,
-4.3415e-02f,  1.8663e-01f, -1.6554e-01f,
 1.0102e-01f,  6.3466e-02f,  1.5225e-01f,
 2.1692e-01f,  1.9860e-01f, -7.0456e-02f,
-1.6406e-03f, -2.7834e-01f, -3.5449e-01f,
-3.0140e-01f, -4.2348e-01f, -5.8263e-01f,
 2.3140e-01f, -2.6843e-01f, -1.1069e-01f,
-9.1484e-02f,  1.1486e-02f,  5.6396e-02f
};

__constant float biasL1[8] = 
{
-9.0964e-02f,  2.1136e-01f, -1.2011e-02f, -4.5657e-38f, -1.4443e-01f, 1.8968e-01f, -2.9027e-02f,  1.6199e-01f
};

__constant float kernelsL[8][9 * 8 * 8] = 
{
{
 4.4561e-02f,  4.3527e-01f, -8.9737e-02f,
-4.9011e-03f,  1.4879e-01f, -8.2210e-02f,
-1.7593e-02f,  4.9294e-02f,  1.8058e-01f,
-3.3827e-02f, -7.9055e-02f,  2.6982e-01f,
-5.2485e-02f, -4.2046e-01f, -5.6838e-02f,
 1.0919e-01f, -7.3141e-02f,  9.4797e-02f,
 6.2764e-02f,  2.5475e-01f,  1.3705e-01f,
 2.0997e-01f,  7.3360e-01f,  2.0801e-01f,
-1.1500e-01f,  3.1245e-01f,  6.7457e-01f,
-5.1481e-39f, -5.1520e-39f, -4.9367e-39f,
-5.1383e-39f, -5.1642e-39f, -4.9479e-39f,
-5.1323e-39f, -5.1859e-39f, -4.9547e-39f,
 1.3849e-01f,  1.1564e-01f, -1.8175e-01f,
-5.5355e-03f, -1.5117e-01f, -2.4654e-01f,
 8.1590e-03f, -1.1681e-01f,  3.4700e-05f,
-2.5950e-01f, -1.4182e-01f,  3.1814e-01f,
 1.7662e-01f,  1.8420e-01f, -1.5181e-01f,
 7.6233e-02f, -7.8372e-02f, -3.1968e-01f,
-4.5770e-01f,  4.1562e-02f,  1.3721e-01f,
-5.8444e-02f,  3.3148e-02f, -2.3370e-01f,
 1.5374e-01f, -1.1162e-01f, -7.4099e-03f,
-1.5716e-01f, -1.8356e-01f,  2.1114e-02f,
-3.2233e-01f,  2.1064e-02f,  2.7019e-01f,
-1.3702e-01f,  2.6969e-01f,  2.1033e-01f,
 8.9027e-02f, -7.9969e-02f,  1.0096e-01f,
 6.6773e-02f,  3.9558e-02f, -7.4944e-02f,
-5.9789e-02f,  1.2265e-01f,  3.3873e-02f,
-9.7157e-03f,  9.2906e-02f,  6.0300e-02f,
-2.2104e-03f,  6.8198e-02f, -1.2931e-01f,
 8.9288e-02f, -1.2554e-01f, -4.3270e-02f,
 1.0660e-01f,  1.1609e-02f, -1.2415e-01f,
 2.6372e-02f, -3.6311e-02f,  1.5625e-01f,
-7.9595e-02f, -3.3662e-01f, -4.0760e-01f,
-2.9566e-39f, -2.8760e-39f, -2.8816e-39f,
-2.9566e-39f, -2.8964e-39f, -2.9115e-39f,
-2.9566e-39f, -2.9179e-39f, -2.9130e-39f,
 7.9255e-02f,  9.4548e-02f,  8.8155e-02f,
-2.8163e-02f,  1.2428e-01f, -6.4973e-03f,
 7.7875e-02f,  7.4765e-02f, -5.2405e-02f,
-1.4886e-02f, -7.1499e-02f, -7.0719e-02f,
 9.7562e-02f,  9.0948e-02f, -5.6588e-02f,
-1.2872e-02f, -6.6390e-02f, -6.4147e-02f,
 9.8262e-02f, -2.4215e-01f, -1.7051e-01f,
 1.8096e-01f,  1.8106e-01f,  1.3108e-01f,
 2.0649e-01f,  1.2242e-01f,  3.7225e-02f,
-2.5125e-01f, -1.0073e-01f,  4.5330e-01f,
 1.8588e-01f, -2.6809e-01f, -1.5709e-01f,
 4.7668e-01f, -2.4208e-01f, -6.6012e-01f,
 1.3561e-01f,  5.4109e-02f,  6.1899e-02f,
-1.9605e-02f,  1.1349e-01f,  3.5781e-02f,
 3.5513e-03f,  3.1212e-02f, -6.0399e-02f,
 5.9258e-02f, -1.8175e-02f,  7.3714e-02f,
 2.0052e-02f,  4.3245e-02f, -5.0879e-03f,
-1.1082e-02f, -1.0753e-01f, -1.7896e-03f,
 2.9139e-02f,  2.2747e-01f, -6.4075e-02f,
 7.3097e-02f,  1.5703e-01f, -5.3815e-01f,
 1.0620e-01f, -1.1386e-01f,  1.7103e-01f,
-3.8728e-39f, -3.8299e-39f, -3.8320e-39f,
-3.9065e-39f, -3.8445e-39f, -3.8135e-39f,
-3.8838e-39f, -3.8114e-39f, -3.8255e-39f,
 2.3253e-02f,  6.9893e-02f,  1.4774e-01f,
 9.6087e-02f,  2.3102e-03f, -3.4449e-02f,
 2.6819e-02f,  1.0254e-01f, -2.8200e-02f,
 3.9553e-02f,  4.7191e-05f, -5.5558e-02f,
 4.1641e-02f,  5.8706e-02f, -1.0337e-01f,
 1.1291e-01f,  5.9622e-02f,  7.0677e-02f,
-2.5162e-01f,  7.6659e-02f,  1.7245e-01f,
-5.8522e-02f,  1.4365e-01f,  2.1189e-01f,
-2.8897e-02f, -5.7365e-02f,  1.4232e-01f,
 1.7854e-02f,  1.7404e-03f, -8.7356e-03f,
-6.0777e-02f, -6.2687e-02f, -1.1500e-02f,
-1.6468e-01f, -2.5058e-01f, -1.2798e-01f,
 2.3193e-02f,  1.7209e-01f,  1.6687e-01f,
-3.4483e-02f, -1.6846e-02f,  2.5930e-02f,
 1.4410e-01f,  4.2932e-02f, -5.0149e-03f,
 4.7269e-02f,  1.1276e-01f, -9.2701e-03f,
 1.5323e-02f,  1.3552e-02f,  9.0256e-02f,
-8.9393e-03f,  7.0903e-02f, -6.9379e-02f,
 1.8645e-01f,  1.0543e-01f, -1.5590e-01f,
 2.1056e-01f,  1.1051e-01f, -1.5514e-01f,
-7.0484e-02f, -1.5153e-01f, -5.0873e-01f,
 3.2730e-39f,  3.2358e-39f,  3.1222e-39f,
 3.2642e-39f,  3.2358e-39f,  3.0921e-39f,
 3.2730e-39f,  3.2358e-39f,  3.0899e-39f,
 1.2225e-02f,  1.2386e-01f,  6.7712e-02f,
 3.1263e-02f,  1.3617e-01f,  1.5352e-01f,
 2.3405e-02f,  8.5466e-02f,  8.7303e-02f,
-2.0372e-02f,  8.3465e-02f, -7.4233e-02f,
 1.2269e-01f,  8.4046e-02f, -3.6869e-02f,
 1.0242e-01f,  7.3218e-02f, -1.1496e-01f,
-1.4539e-01f, -2.3923e-01f, -2.2818e-01f,
-3.2368e-02f, -7.4360e-02f,  2.3493e-02f,
 1.7004e-01f,  6.2924e-02f,  8.9327e-02f,
-1.1449e-01f, -1.4973e-03f, -7.0451e-03f,
-9.3205e-02f, -1.0312e-01f,  4.6503e-02f,
-2.2148e-01f, -1.8111e-01f, -1.1992e-01f,
 9.8140e-02f,  9.9823e-02f, -2.0282e-02f,
-8.1973e-02f,  1.4255e-01f, -5.2392e-02f,
 8.0350e-03f, -4.8299e-02f, -7.7908e-02f,
 4.2383e-02f,  3.0707e-02f,  2.8560e-02f,
 1.0437e-01f,  6.1290e-02f, -9.7796e-02f,
-1.7125e-02f, -1.3572e-01f, -1.5345e-01f,
-1.3292e-01f,  2.9477e-02f,  6.8032e-02f,
 1.5741e-01f,  4.0258e-01f,  2.5838e-01f,
 1.3948e-01f,  3.5713e-01f, -3.9825e-01f,
-1.9224e-39f, -2.4076e-39f, -2.4529e-39f,
-1.9181e-39f, -1.9894e-39f, -4.0240e-39f,
-1.9335e-39f, -2.3920e-39f, -4.0147e-39f,
-2.1714e-02f, -3.5299e-02f, -7.5803e-03f,
-2.4087e-02f,  7.5265e-02f,  7.6697e-02f,
 4.5309e-02f,  8.9529e-02f,  7.6510e-03f,
 1.0813e-02f,  3.1294e-02f, -2.5907e-02f,
 1.1962e-02f, -6.8664e-03f, -1.4084e-01f,
 7.7013e-02f, -1.2305e-01f, -6.7800e-02f,
-9.7392e-02f,  4.4082e-02f,  1.4473e-01f,
 4.9436e-02f,  2.8859e-01f,  2.8252e-01f,
-3.5828e-02f, -7.5616e-02f,  2.4875e-01f,
-6.7684e-02f,  1.1290e-01f,  4.2827e-02f,
-1.0860e-01f,  1.2952e-01f,  5.9784e-01f,
-3.5402e-01f, -3.9558e-02f, -6.0775e-01f,
-1.2854e-02f,  1.5240e-01f,  1.4115e-01f,
-2.8134e-02f, -1.2939e-02f, -2.6203e-02f,
 1.1300e-01f,  1.4481e-01f, -5.1454e-02f,
 1.2688e-01f,  2.8536e-02f,  9.4877e-02f,
 9.6033e-02f, -1.3901e-02f,  6.0035e-02f,
-1.1249e-01f,  4.3971e-02f, -1.0918e-01f,
 8.2500e-02f,  2.1413e-01f,  3.9015e-02f,
 1.8361e-01f,  2.5271e-01f, -2.2794e-01f,
-8.1195e-02f, -1.2269e-01f, -2.6097e-01f,
 7.6827e-39f,  7.7882e-39f,  7.6893e-39f,
 7.7006e-39f,  7.7857e-39f,  7.7384e-39f,
 7.6985e-39f,  7.7712e-39f,  7.7399e-39f,
 1.4458e-02f,  1.0801e-01f,  1.5906e-01f,
-1.4676e-02f,  1.3699e-01f,  9.2460e-02f,
-3.6479e-02f,  1.4529e-01f, -2.8681e-02f,
-3.3251e-02f, -7.3096e-02f, -1.4330e-01f,
 5.7009e-02f, -3.1905e-02f, -1.2035e-01f,
 1.1838e-01f,  5.7011e-02f,  2.0800e-02f,
-1.1567e-02f, -2.2125e-01f, -9.3953e-02f,
-7.5378e-02f, -1.2069e-01f,  1.3217e-01f,
-7.7357e-02f, -1.3171e-01f,  1.2776e-01f,
-1.1397e-01f, -3.5183e-02f,  2.2994e-02f,
-6.5101e-02f, -1.5019e-01f, -2.7451e-02f,
-2.4260e-01f, -1.3543e-01f, -1.9889e-02f,
-1.9798e-39f, -3.5282e-40f, -1.9216e-39f,
-1.9140e-39f, -1.9370e-39f, -1.9943e-39f,
-1.8623e-39f, -1.8665e-39f, -1.9320e-39f,
-4.8850e-39f, -5.0283e-39f, -4.9987e-39f,
-5.0868e-39f, -5.0814e-39f, -5.0779e-39f,
-5.2489e-39f, -5.1086e-39f, -5.1234e-39f,
-2.9120e-39f, -3.0278e-39f, -2.9633e-39f,
 1.3186e-39f,  6.0555e-39f,  6.0419e-39f,
-5.5922e-39f, -8.5992e-40f, -2.8529e-39f,
-3.4668e-39f, -3.5127e-39f, -3.4668e-39f,
-3.2831e-39f, -3.4668e-39f, -3.6734e-39f,
-3.2142e-39f, -3.2831e-39f, -3.5816e-39f,
 1.3445e-39f,  1.3621e-39f,  1.3375e-39f,
 1.4539e-39f, -2.2695e-40f,  1.4522e-39f,
 1.3563e-39f,  1.3339e-39f,  1.3001e-39f,
-4.4670e-39f, -4.4026e-39f, -4.3159e-39f,
-4.5047e-39f, -4.3505e-39f, -2.7259e-39f,
-4.5265e-39f, -4.4721e-39f, -4.4990e-39f,
-1.9864e-39f, -4.1379e-39f, -3.7189e-39f,
 5.2465e-39f,  2.5220e-39f,  1.5639e-39f,
-3.9760e-39f, -5.7033e-39f, -4.0978e-39f,
-6.3745e-40f, -4.7511e-39f,  2.3456e-39f,
-1.5164e-39f,  5.0431e-39f,  5.1197e-39f,
 8.7052e-40f,  1.4947e-39f, -1.1546e-39f,
 5.3140e-02f,  1.0281e-01f,  1.4767e-01f,
-6.1530e-02f, -9.4166e-02f,  4.8671e-02f,
 5.6787e-03f, -1.4551e-01f,  1.5614e-02f,
-3.4826e-02f, -5.1148e-02f,  9.7079e-02f,
-1.3603e-02f, -1.2249e-01f, -1.9330e-02f,
-6.8184e-02f, -1.4344e-01f, -9.4023e-03f,
-7.4629e-02f,  3.9634e-02f,  1.3445e-01f,
 4.2153e-02f,  7.1129e-01f,  2.8703e-02f,
 7.8247e-02f,  7.2210e-01f, -6.6198e-01f,
-6.1010e-39f, -6.2892e-39f, -6.4008e-39f,
-6.0825e-39f, -6.3221e-39f, -6.3883e-39f,
-1.4962e-39f, -1.1702e-39f, -1.2143e-39f,
 5.5512e-02f, -2.1522e-02f,  1.0866e-01f,
-9.2812e-02f, -3.5119e-02f,  1.1396e-01f,
-1.3922e-01f,  6.7287e-02f, -5.5626e-02f,
-2.0492e-01f,  8.1441e-02f, -1.3513e-01f,
 4.7447e-02f,  2.0081e-01f, -3.1249e-01f,
-1.8546e-02f,  2.0680e-01f,  7.3979e-02f,
 8.8928e-02f, -4.3606e-01f, -8.4823e-02f,
-5.6133e-02f,  3.5132e-01f,  1.8633e-01f,
-4.3855e-03f,  5.4869e-02f,  1.1658e-01f,
 1.7423e-01f, -5.3107e-02f,  2.2925e-02f,
-1.7622e-01f,  4.4453e-02f,  2.8131e-02f,
 2.6863e-01f, -2.9085e-01f, -1.5098e-01f
}
,
{
-2.4230e-40f,  5.4425e-39f,  3.4517e-39f,
-1.9803e-39f, -1.5207e-39f, -3.5630e-39f,
-4.9409e-39f, -2.9280e-39f,  7.7966e-40f,
 2.4867e-39f, -2.1848e-39f,  3.2524e-39f,
-6.2860e-39f,  4.0411e-39f, -3.6956e-39f,
-3.3384e-39f, -1.0908e-39f,  5.4261e-39f,
-3.6691e-40f,  9.4949e-40f, -1.7279e-39f,
-1.0644e-39f, -2.1371e-39f, -2.5125e-39f,
 2.9368e-39f, -5.3820e-39f, -3.9771e-40f,
-1.4703e-39f, -3.6960e-39f, -4.4161e-39f,
 8.2800e-40f, -4.9175e-39f,  3.1868e-39f,
 5.5703e-39f, -3.0263e-39f, -1.6991e-39f,
 5.2691e-39f,  4.8127e-39f,  4.1346e-39f,
-1.3013e-39f, -1.7101e-39f, -3.5467e-39f,
 1.1496e-39f,  2.0938e-39f, -4.2970e-39f,
-5.5314e-39f,  6.4852e-40f, -5.0870e-39f,
 3.9377e-39f, -4.1683e-39f, -3.5404e-40f,
-3.6188e-39f,  5.4657e-39f,  2.1279e-39f,
 3.4090e-40f,  2.4425e-40f,  9.3423e-41f,
-2.3450e-39f,  3.1518e-40f,  4.3061e-40f,
-2.6175e-39f, -2.4696e-39f, -2.3755e-39f,
 2.2764e-39f, -4.4934e-39f,  8.5722e-40f,
 5.1798e-39f,  2.7072e-39f,  5.3750e-39f,
 5.4335e-40f,  3.8556e-39f, -3.4799e-39f,
-4.8963e-39f, -1.1413e-39f, -5.3918e-40f,
 6.1843e-39f, -1.8521e-39f, -1.3450e-39f,
-2.0906e-39f, -3.2544e-39f, -2.8205e-39f,
 5.3550e-39f, -3.0202e-39f, -3.4181e-39f,
-3.0043e-39f, -3.2900e-39f, -3.2915e-39f,
 6.1849e-39f, -3.3421e-39f, -3.3995e-39f,
-4.8657e-39f, -4.7034e-39f, -4.7467e-39f,
-4.6555e-39f, -4.6045e-39f, -4.6954e-39f,
-4.8886e-39f, -4.7333e-39f, -4.7805e-39f,
-2.0900e-39f, -1.9429e-39f, -2.0572e-39f,
-2.0270e-39f, -1.9074e-39f, -1.9275e-39f,
-2.1243e-39f, -2.1134e-39f, -2.1539e-39f,
-4.4175e-39f, -4.6412e-39f, -4.6582e-39f,
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-5.8004e-02f,  5.8991e-02f, -6.2149e-03f,
-1.3034e-01f,  1.5540e-01f, -5.2558e-02f,
 8.1594e-02f,  3.5570e-01f,  2.1220e-02f,
 1.4977e-02f,  2.4493e-03f, -4.0627e-02f,
 1.1402e-01f,  6.6962e-02f,  1.1150e-01f,
 1.1824e-01f,  1.1492e-01f,  1.1219e-01f,
 6.6067e-02f,  6.9639e-02f, -8.1836e-02f,
-2.7144e-02f,  1.4677e-01f, -5.9261e-02f,
 4.4573e-03f,  2.6235e-01f, -7.4379e-01f,
-8.3569e-03f,  9.4465e-02f, -6.5653e-03f,
 2.1095e-02f, -1.8853e-02f,  6.7972e-02f,
 1.2957e-01f,  3.0122e-02f, -1.0061e-02f,
-3.4832e-02f,  8.5404e-02f,  5.7663e-02f,
-5.0400e-02f, -1.2050e-01f, -2.3344e-01f,
 1.4977e-01f,  7.8806e-02f,  6.0771e-03f,
 5.6483e-02f,  6.3927e-02f, -5.8376e-03f,
-2.8124e-01f,  5.2581e-02f, -1.3918e-04f,
-1.4341e-01f,  3.6558e-01f,  4.7332e-02f,
-3.9089e-02f,  8.4188e-02f,  2.7058e-02f
}
};

__constant float biasL[8][8] = 
{
{
 7.2678e-02f,  8.5350e-03f,  5.0400e-02f,  2.6268e-02f,  6.2434e-02f, 1.0483e-01f, -7.1650e-39f,  1.0062e-01f
}
,
{
-4.9844e-39f, -1.8567e-39f,  6.0627e-04f, -1.9234e-38f,  1.8331e-02f, -1.1364e-01f, -8.3962e-03f, -1.7372e-04f
}
,
{
-0.0091f, -0.0055f,  0.0237f,  0.0093f, -0.0479f,  0.0188f, -0.0034f,  0.0399f
}
,
{
 6.5694e-03f, -2.2259e-01f, -1.1226e-02f, -8.0327e-02f, -1.0615e-36f, 1.0402e-02f,  7.6246e-03f, -6.5940e-02f
}
,
{
 5.0711e-02f,  7.1911e-02f,  2.5293e-02f, -1.5608e-02f,  5.3835e-02f, -1.6967e-38f,  2.2243e-02f,  3.2742e-02f
}
,
{
 1.5629e-02f,  2.9703e-02f,  2.6412e-02f,  1.2301e-02f,  1.8654e-01f, -7.2260e-03f,  2.4613e-02f, -3.1853e-38f
}
,
{
-0.0030f, -0.0123f,  0.0348f,  0.0277f, -0.0152f,  0.0005f, -0.0124f, -0.0209f
}
,
{
7.4856e-03f, 7.2931e-04f, 8.3015e-03f, 6.4820e-03f, 2.4008e-04f, 7.0377e-06f, 1.7948e-03f, 8.9869e-03f
}
};

__constant float kernelsL10[4 * 8] = 
{
 0.4240f,  0.4165f,
 0.1648f,  0.1909f,
-0.0985f, -0.4455f,
 0.4639f, -0.0533f,
-0.1368f,  0.4413f,
 0.2539f,  0.3294f,
 0.2458f, -0.3256f,
-0.0479f,  0.3200f,
-0.3977f, -0.0422f,
-0.2736f,  0.1053f,
 0.3902f,  0.0594f,
-0.0721f, -0.2988f,
 0.0495f,  0.1309f,
-0.1703f,  0.0033f,
 0.3061f,  0.1827f,
 0.2443f, -0.1259f
};


__kernel void conv1To8(
    __read_only image2d_t srcImg, 
    __write_only image2d_array_t tmpImgOut)
{
    const int x = get_global_id(0), y = get_global_id(1);
    if(x >= get_image_width(srcImg) || y >= get_image_height(srcImg))
        return;
    
    const int2 imgIndex1 = (int2)(0, 0);
    const int2 imgIndex2 = (int2)(1, 0);
    int2 coord = (int2)(x, y);

    float4 tl = read_imagef(srcImg, samplerN, (int2)(x-1, y-1));
    float4 tc = read_imagef(srcImg, samplerN, (int2)(x, y-1));
    float4 tr = read_imagef(srcImg, samplerN, (int2)(x+1, y-1));
    float4 ml = read_imagef(srcImg, samplerN, (int2)(x-1, y));
    float4 mc = read_imagef(srcImg, samplerN, coord);
    float4 mr = read_imagef(srcImg, samplerN, (int2)(x+1, y));
    float4 bl = read_imagef(srcImg, samplerN, (int2)(x-1, y+1));
    float4 bc = read_imagef(srcImg, samplerN, (int2)(x, y+1));
    float4 br = read_imagef(srcImg, samplerN, (int2)(x+1, y+1));

    float4 c1234 = RELU((float4)(
        tl.x * kernelsL1[0*9+0] + tc.x * kernelsL1[0*9+1] + tr.x * kernelsL1[0*9+2] +
        ml.x * kernelsL1[0*9+3] + mc.x * kernelsL1[0*9+4] + mr.x * kernelsL1[0*9+5] +
        bl.x * kernelsL1[0*9+6] + bc.x * kernelsL1[0*9+7] + br.x * kernelsL1[0*9+8] + biasL1[0],

        tl.x * kernelsL1[1*9+0] + tc.x * kernelsL1[1*9+1] + tr.x * kernelsL1[1*9+2] +
        ml.x * kernelsL1[1*9+3] + mc.x * kernelsL1[1*9+4] + mr.x * kernelsL1[1*9+5] +
        bl.x * kernelsL1[1*9+6] + bc.x * kernelsL1[1*9+7] + br.x * kernelsL1[1*9+8] + biasL1[1],

        tl.x * kernelsL1[2*9+0] + tc.x * kernelsL1[2*9+1] + tr.x * kernelsL1[2*9+2] +
        ml.x * kernelsL1[2*9+3] + mc.x * kernelsL1[2*9+4] + mr.x * kernelsL1[2*9+5] +
        bl.x * kernelsL1[2*9+6] + bc.x * kernelsL1[2*9+7] + br.x * kernelsL1[2*9+8] + biasL1[2],

        tl.x * kernelsL1[3*9+0] + tc.x * kernelsL1[3*9+1] + tr.x * kernelsL1[3*9+2] +
        ml.x * kernelsL1[3*9+3] + mc.x * kernelsL1[3*9+4] + mr.x * kernelsL1[3*9+5] +
        bl.x * kernelsL1[3*9+6] + bc.x * kernelsL1[3*9+7] + br.x * kernelsL1[3*9+8] + biasL1[3]
    ));
    float4 c5678 = RELU((float4)(
        tl.x * kernelsL1[4*9+0] + tc.x * kernelsL1[4*9+1] + tr.x * kernelsL1[4*9+2] +
        ml.x * kernelsL1[4*9+3] + mc.x * kernelsL1[4*9+4] + mr.x * kernelsL1[4*9+5] +
        bl.x * kernelsL1[4*9+6] + bc.x * kernelsL1[4*9+7] + br.x * kernelsL1[4*9+8] + biasL1[4],

        tl.x * kernelsL1[5*9+0] + tc.x * kernelsL1[5*9+1] + tr.x * kernelsL1[5*9+2] +
        ml.x * kernelsL1[5*9+3] + mc.x * kernelsL1[5*9+4] + mr.x * kernelsL1[5*9+5] +
        bl.x * kernelsL1[5*9+6] + bc.x * kernelsL1[5*9+7] + br.x * kernelsL1[5*9+8] + biasL1[5],

        tl.x * kernelsL1[6*9+0] + tc.x * kernelsL1[6*9+1] + tr.x * kernelsL1[6*9+2] +
        ml.x * kernelsL1[6*9+3] + mc.x * kernelsL1[6*9+4] + mr.x * kernelsL1[6*9+5] +
        bl.x * kernelsL1[6*9+6] + bc.x * kernelsL1[6*9+7] + br.x * kernelsL1[6*9+8] + biasL1[6],

        tl.x * kernelsL1[7*9+0] + tc.x * kernelsL1[7*9+1] + tr.x * kernelsL1[7*9+2] +
        ml.x * kernelsL1[7*9+3] + mc.x * kernelsL1[7*9+4] + mr.x * kernelsL1[7*9+5] +
        bl.x * kernelsL1[7*9+6] + bc.x * kernelsL1[7*9+7] + br.x * kernelsL1[7*9+8] + biasL1[7]
    ));

    write_imagef(tmpImgOut, (int4)(coord, imgIndex1), c1234);
    write_imagef(tmpImgOut, (int4)(coord, imgIndex2), c5678);
}

__kernel void conv8To8(
    __read_only image2d_array_t tmpImgIn,
    __write_only image2d_array_t tmpImgOut,
    int l)
{
    const int x = get_global_id(0), y = get_global_id(1);
    if(x >= get_image_width(tmpImgIn) || y >= get_image_height(tmpImgIn))
        return;

    const int2 imgIndex1 = (int2)(0, 0);
    const int2 imgIndex2 = (int2)(1, 0);
    int4 coord1 = (int4)(x, y, imgIndex1);
    int4 coord2 = (int4)(x, y, imgIndex2);

    float4 tl1 = read_imagef(tmpImgIn, samplerN, (int4)(x-1, y-1, imgIndex1));
    float4 tc1 = read_imagef(tmpImgIn, samplerN, (int4)(x, y-1, imgIndex1));
    float4 tr1 = read_imagef(tmpImgIn, samplerN, (int4)(x+1, y-1, imgIndex1));
    float4 ml1 = read_imagef(tmpImgIn, samplerN, (int4)(x-1, y, imgIndex1));
    float4 mc1 = read_imagef(tmpImgIn, samplerN, coord1);
    float4 mr1 = read_imagef(tmpImgIn, samplerN, (int4)(x+1, y, imgIndex1));
    float4 bl1 = read_imagef(tmpImgIn, samplerN, (int4)(x-1, y+1, imgIndex1));
    float4 bc1 = read_imagef(tmpImgIn, samplerN, (int4)(x, y+1, imgIndex1));
    float4 br1 = read_imagef(tmpImgIn, samplerN, (int4)(x+1, y+1, imgIndex1));

    float4 tl2 = read_imagef(tmpImgIn, samplerN, (int4)(x-1, y-1, imgIndex2));
    float4 tc2 = read_imagef(tmpImgIn, samplerN, (int4)(x, y-1, imgIndex2));
    float4 tr2 = read_imagef(tmpImgIn, samplerN, (int4)(x+1, y-1, imgIndex2));
    float4 ml2 = read_imagef(tmpImgIn, samplerN, (int4)(x-1, y, imgIndex2));
    float4 mc2 = read_imagef(tmpImgIn, samplerN, coord2);
    float4 mr2 = read_imagef(tmpImgIn, samplerN, (int4)(x+1, y, imgIndex2));
    float4 bl2 = read_imagef(tmpImgIn, samplerN, (int4)(x-1, y+1, imgIndex2));
    float4 bc2 = read_imagef(tmpImgIn, samplerN, (int4)(x, y+1, imgIndex2));
    float4 br2 = read_imagef(tmpImgIn, samplerN, (int4)(x+1, y+1, imgIndex2));
    
    float4 c1234 = RELU((float4)(
        tl1.x * kernelsL[l][0*72+0*9+0] + tc1.x * kernelsL[l][0*72+0*9+1] + tr1.x * kernelsL[l][0*72+0*9+2] +
        ml1.x * kernelsL[l][0*72+0*9+3] + mc1.x * kernelsL[l][0*72+0*9+4] + mr1.x * kernelsL[l][0*72+0*9+5] +
        bl1.x * kernelsL[l][0*72+0*9+6] + bc1.x * kernelsL[l][0*72+0*9+7] + br1.x * kernelsL[l][0*72+0*9+8] + 

        tl1.y * kernelsL[l][0*72+1*9+0] + tc1.y * kernelsL[l][0*72+1*9+1] + tr1.y * kernelsL[l][0*72+1*9+2] +
        ml1.y * kernelsL[l][0*72+1*9+3] + mc1.y * kernelsL[l][0*72+1*9+4] + mr1.y * kernelsL[l][0*72+1*9+5] +
        bl1.y * kernelsL[l][0*72+1*9+6] + bc1.y * kernelsL[l][0*72+1*9+7] + br1.y * kernelsL[l][0*72+1*9+8] + 

        tl1.z * kernelsL[l][0*72+2*9+0] + tc1.z * kernelsL[l][0*72+2*9+1] + tr1.z * kernelsL[l][0*72+2*9+2] +
        ml1.z * kernelsL[l][0*72+2*9+3] + mc1.z * kernelsL[l][0*72+2*9+4] + mr1.z * kernelsL[l][0*72+2*9+5] +
        bl1.z * kernelsL[l][0*72+2*9+6] + bc1.z * kernelsL[l][0*72+2*9+7] + br1.z * kernelsL[l][0*72+2*9+8] + 

        tl1.w * kernelsL[l][0*72+3*9+0] + tc1.w * kernelsL[l][0*72+3*9+1] + tr1.w * kernelsL[l][0*72+3*9+2] +
        ml1.w * kernelsL[l][0*72+3*9+3] + mc1.w * kernelsL[l][0*72+3*9+4] + mr1.w * kernelsL[l][0*72+3*9+5] +
        bl1.w * kernelsL[l][0*72+3*9+6] + bc1.w * kernelsL[l][0*72+3*9+7] + br1.w * kernelsL[l][0*72+3*9+8] +

        tl2.x * kernelsL[l][0*72+4*9+0] + tc2.x * kernelsL[l][0*72+4*9+1] + tr2.x * kernelsL[l][0*72+4*9+2] +
        ml2.x * kernelsL[l][0*72+4*9+3] + mc2.x * kernelsL[l][0*72+4*9+4] + mr2.x * kernelsL[l][0*72+4*9+5] +
        bl2.x * kernelsL[l][0*72+4*9+6] + bc2.x * kernelsL[l][0*72+4*9+7] + br2.x * kernelsL[l][0*72+4*9+8] + 

        tl2.y * kernelsL[l][0*72+5*9+0] + tc2.y * kernelsL[l][0*72+5*9+1] + tr2.y * kernelsL[l][0*72+5*9+2] +
        ml2.y * kernelsL[l][0*72+5*9+3] + mc2.y * kernelsL[l][0*72+5*9+4] + mr2.y * kernelsL[l][0*72+5*9+5] +
        bl2.y * kernelsL[l][0*72+5*9+6] + bc2.y * kernelsL[l][0*72+5*9+7] + br2.y * kernelsL[l][0*72+5*9+8] + 

        tl2.z * kernelsL[l][0*72+6*9+0] + tc2.z * kernelsL[l][0*72+6*9+1] + tr2.z * kernelsL[l][0*72+6*9+2] +
        ml2.z * kernelsL[l][0*72+6*9+3] + mc2.z * kernelsL[l][0*72+6*9+4] + mr2.z * kernelsL[l][0*72+6*9+5] +
        bl2.z * kernelsL[l][0*72+6*9+6] + bc2.z * kernelsL[l][0*72+6*9+7] + br2.z * kernelsL[l][0*72+6*9+8] + 

        tl2.w * kernelsL[l][0*72+7*9+0] + tc2.w * kernelsL[l][0*72+7*9+1] + tr2.w * kernelsL[l][0*72+7*9+2] +
        ml2.w * kernelsL[l][0*72+7*9+3] + mc2.w * kernelsL[l][0*72+7*9+4] + mr2.w * kernelsL[l][0*72+7*9+5] +
        bl2.w * kernelsL[l][0*72+7*9+6] + bc2.w * kernelsL[l][0*72+7*9+7] + br2.w * kernelsL[l][0*72+7*9+8] + biasL[l][0]
        ,
        tl1.x * kernelsL[l][1*72+0*9+0] + tc1.x * kernelsL[l][1*72+0*9+1] + tr1.x * kernelsL[l][1*72+0*9+2] +
        ml1.x * kernelsL[l][1*72+0*9+3] + mc1.x * kernelsL[l][1*72+0*9+4] + mr1.x * kernelsL[l][1*72+0*9+5] +
        bl1.x * kernelsL[l][1*72+0*9+6] + bc1.x * kernelsL[l][1*72+0*9+7] + br1.x * kernelsL[l][1*72+0*9+8] + 

        tl1.y * kernelsL[l][1*72+1*9+0] + tc1.y * kernelsL[l][1*72+1*9+1] + tr1.y * kernelsL[l][1*72+1*9+2] +
        ml1.y * kernelsL[l][1*72+1*9+3] + mc1.y * kernelsL[l][1*72+1*9+4] + mr1.y * kernelsL[l][1*72+1*9+5] +
        bl1.y * kernelsL[l][1*72+1*9+6] + bc1.y * kernelsL[l][1*72+1*9+7] + br1.y * kernelsL[l][1*72+1*9+8] + 

        tl1.z * kernelsL[l][1*72+2*9+0] + tc1.z * kernelsL[l][1*72+2*9+1] + tr1.z * kernelsL[l][1*72+2*9+2] +
        ml1.z * kernelsL[l][1*72+2*9+3] + mc1.z * kernelsL[l][1*72+2*9+4] + mr1.z * kernelsL[l][1*72+2*9+5] +
        bl1.z * kernelsL[l][1*72+2*9+6] + bc1.z * kernelsL[l][1*72+2*9+7] + br1.z * kernelsL[l][1*72+2*9+8] + 

        tl1.w * kernelsL[l][1*72+3*9+0] + tc1.w * kernelsL[l][1*72+3*9+1] + tr1.w * kernelsL[l][1*72+3*9+2] +
        ml1.w * kernelsL[l][1*72+3*9+3] + mc1.w * kernelsL[l][1*72+3*9+4] + mr1.w * kernelsL[l][1*72+3*9+5] +
        bl1.w * kernelsL[l][1*72+3*9+6] + bc1.w * kernelsL[l][1*72+3*9+7] + br1.w * kernelsL[l][1*72+3*9+8] +

        tl2.x * kernelsL[l][1*72+4*9+0] + tc2.x * kernelsL[l][1*72+4*9+1] + tr2.x * kernelsL[l][1*72+4*9+2] +
        ml2.x * kernelsL[l][1*72+4*9+3] + mc2.x * kernelsL[l][1*72+4*9+4] + mr2.x * kernelsL[l][1*72+4*9+5] +
        bl2.x * kernelsL[l][1*72+4*9+6] + bc2.x * kernelsL[l][1*72+4*9+7] + br2.x * kernelsL[l][1*72+4*9+8] + 

        tl2.y * kernelsL[l][1*72+5*9+0] + tc2.y * kernelsL[l][1*72+5*9+1] + tr2.y * kernelsL[l][1*72+5*9+2] +
        ml2.y * kernelsL[l][1*72+5*9+3] + mc2.y * kernelsL[l][1*72+5*9+4] + mr2.y * kernelsL[l][1*72+5*9+5] +
        bl2.y * kernelsL[l][1*72+5*9+6] + bc2.y * kernelsL[l][1*72+5*9+7] + br2.y * kernelsL[l][1*72+5*9+8] + 

        tl2.z * kernelsL[l][1*72+6*9+0] + tc2.z * kernelsL[l][1*72+6*9+1] + tr2.z * kernelsL[l][1*72+6*9+2] +
        ml2.z * kernelsL[l][1*72+6*9+3] + mc2.z * kernelsL[l][1*72+6*9+4] + mr2.z * kernelsL[l][1*72+6*9+5] +
        bl2.z * kernelsL[l][1*72+6*9+6] + bc2.z * kernelsL[l][1*72+6*9+7] + br2.z * kernelsL[l][1*72+6*9+8] + 

        tl2.w * kernelsL[l][1*72+7*9+0] + tc2.w * kernelsL[l][1*72+7*9+1] + tr2.w * kernelsL[l][1*72+7*9+2] +
        ml2.w * kernelsL[l][1*72+7*9+3] + mc2.w * kernelsL[l][1*72+7*9+4] + mr2.w * kernelsL[l][1*72+7*9+5] +
        bl2.w * kernelsL[l][1*72+7*9+6] + bc2.w * kernelsL[l][1*72+7*9+7] + br2.w * kernelsL[l][1*72+7*9+8] + biasL[l][1]
        ,
        tl1.x * kernelsL[l][2*72+0*9+0] + tc1.x * kernelsL[l][2*72+0*9+1] + tr1.x * kernelsL[l][2*72+0*9+2] +
        ml1.x * kernelsL[l][2*72+0*9+3] + mc1.x * kernelsL[l][2*72+0*9+4] + mr1.x * kernelsL[l][2*72+0*9+5] +
        bl1.x * kernelsL[l][2*72+0*9+6] + bc1.x * kernelsL[l][2*72+0*9+7] + br1.x * kernelsL[l][2*72+0*9+8] + 

        tl1.y * kernelsL[l][2*72+1*9+0] + tc1.y * kernelsL[l][2*72+1*9+1] + tr1.y * kernelsL[l][2*72+1*9+2] +
        ml1.y * kernelsL[l][2*72+1*9+3] + mc1.y * kernelsL[l][2*72+1*9+4] + mr1.y * kernelsL[l][2*72+1*9+5] +
        bl1.y * kernelsL[l][2*72+1*9+6] + bc1.y * kernelsL[l][2*72+1*9+7] + br1.y * kernelsL[l][2*72+1*9+8] + 

        tl1.z * kernelsL[l][2*72+2*9+0] + tc1.z * kernelsL[l][2*72+2*9+1] + tr1.z * kernelsL[l][2*72+2*9+2] +
        ml1.z * kernelsL[l][2*72+2*9+3] + mc1.z * kernelsL[l][2*72+2*9+4] + mr1.z * kernelsL[l][2*72+2*9+5] +
        bl1.z * kernelsL[l][2*72+2*9+6] + bc1.z * kernelsL[l][2*72+2*9+7] + br1.z * kernelsL[l][2*72+2*9+8] + 

        tl1.w * kernelsL[l][2*72+3*9+0] + tc1.w * kernelsL[l][2*72+3*9+1] + tr1.w * kernelsL[l][2*72+3*9+2] +
        ml1.w * kernelsL[l][2*72+3*9+3] + mc1.w * kernelsL[l][2*72+3*9+4] + mr1.w * kernelsL[l][2*72+3*9+5] +
        bl1.w * kernelsL[l][2*72+3*9+6] + bc1.w * kernelsL[l][2*72+3*9+7] + br1.w * kernelsL[l][2*72+3*9+8] +

        tl2.x * kernelsL[l][2*72+4*9+0] + tc2.x * kernelsL[l][2*72+4*9+1] + tr2.x * kernelsL[l][2*72+4*9+2] +
        ml2.x * kernelsL[l][2*72+4*9+3] + mc2.x * kernelsL[l][2*72+4*9+4] + mr2.x * kernelsL[l][2*72+4*9+5] +
        bl2.x * kernelsL[l][2*72+4*9+6] + bc2.x * kernelsL[l][2*72+4*9+7] + br2.x * kernelsL[l][2*72+4*9+8] + 

        tl2.y * kernelsL[l][2*72+5*9+0] + tc2.y * kernelsL[l][2*72+5*9+1] + tr2.y * kernelsL[l][2*72+5*9+2] +
        ml2.y * kernelsL[l][2*72+5*9+3] + mc2.y * kernelsL[l][2*72+5*9+4] + mr2.y * kernelsL[l][2*72+5*9+5] +
        bl2.y * kernelsL[l][2*72+5*9+6] + bc2.y * kernelsL[l][2*72+5*9+7] + br2.y * kernelsL[l][2*72+5*9+8] + 

        tl2.z * kernelsL[l][2*72+6*9+0] + tc2.z * kernelsL[l][2*72+6*9+1] + tr2.z * kernelsL[l][2*72+6*9+2] +
        ml2.z * kernelsL[l][2*72+6*9+3] + mc2.z * kernelsL[l][2*72+6*9+4] + mr2.z * kernelsL[l][2*72+6*9+5] +
        bl2.z * kernelsL[l][2*72+6*9+6] + bc2.z * kernelsL[l][2*72+6*9+7] + br2.z * kernelsL[l][2*72+6*9+8] + 

        tl2.w * kernelsL[l][2*72+7*9+0] + tc2.w * kernelsL[l][2*72+7*9+1] + tr2.w * kernelsL[l][2*72+7*9+2] +
        ml2.w * kernelsL[l][2*72+7*9+3] + mc2.w * kernelsL[l][2*72+7*9+4] + mr2.w * kernelsL[l][2*72+7*9+5] +
        bl2.w * kernelsL[l][2*72+7*9+6] + bc2.w * kernelsL[l][2*72+7*9+7] + br2.w * kernelsL[l][2*72+7*9+8] + biasL[l][2]
        ,
        tl1.x * kernelsL[l][3*72+0*9+0] + tc1.x * kernelsL[l][3*72+0*9+1] + tr1.x * kernelsL[l][3*72+0*9+2] +
        ml1.x * kernelsL[l][3*72+0*9+3] + mc1.x * kernelsL[l][3*72+0*9+4] + mr1.x * kernelsL[l][3*72+0*9+5] +
        bl1.x * kernelsL[l][3*72+0*9+6] + bc1.x * kernelsL[l][3*72+0*9+7] + br1.x * kernelsL[l][3*72+0*9+8] + 

        tl1.y * kernelsL[l][3*72+1*9+0] + tc1.y * kernelsL[l][3*72+1*9+1] + tr1.y * kernelsL[l][3*72+1*9+2] +
        ml1.y * kernelsL[l][3*72+1*9+3] + mc1.y * kernelsL[l][3*72+1*9+4] + mr1.y * kernelsL[l][3*72+1*9+5] +
        bl1.y * kernelsL[l][3*72+1*9+6] + bc1.y * kernelsL[l][3*72+1*9+7] + br1.y * kernelsL[l][3*72+1*9+8] + 

        tl1.z * kernelsL[l][3*72+2*9+0] + tc1.z * kernelsL[l][3*72+2*9+1] + tr1.z * kernelsL[l][3*72+2*9+2] +
        ml1.z * kernelsL[l][3*72+2*9+3] + mc1.z * kernelsL[l][3*72+2*9+4] + mr1.z * kernelsL[l][3*72+2*9+5] +
        bl1.z * kernelsL[l][3*72+2*9+6] + bc1.z * kernelsL[l][3*72+2*9+7] + br1.z * kernelsL[l][3*72+2*9+8] + 

        tl1.w * kernelsL[l][3*72+3*9+0] + tc1.w * kernelsL[l][3*72+3*9+1] + tr1.w * kernelsL[l][3*72+3*9+2] +
        ml1.w * kernelsL[l][3*72+3*9+3] + mc1.w * kernelsL[l][3*72+3*9+4] + mr1.w * kernelsL[l][3*72+3*9+5] +
        bl1.w * kernelsL[l][3*72+3*9+6] + bc1.w * kernelsL[l][3*72+3*9+7] + br1.w * kernelsL[l][3*72+3*9+8] +

        tl2.x * kernelsL[l][3*72+4*9+0] + tc2.x * kernelsL[l][3*72+4*9+1] + tr2.x * kernelsL[l][3*72+4*9+2] +
        ml2.x * kernelsL[l][3*72+4*9+3] + mc2.x * kernelsL[l][3*72+4*9+4] + mr2.x * kernelsL[l][3*72+4*9+5] +
        bl2.x * kernelsL[l][3*72+4*9+6] + bc2.x * kernelsL[l][3*72+4*9+7] + br2.x * kernelsL[l][3*72+4*9+8] + 

        tl2.y * kernelsL[l][3*72+5*9+0] + tc2.y * kernelsL[l][3*72+5*9+1] + tr2.y * kernelsL[l][3*72+5*9+2] +
        ml2.y * kernelsL[l][3*72+5*9+3] + mc2.y * kernelsL[l][3*72+5*9+4] + mr2.y * kernelsL[l][3*72+5*9+5] +
        bl2.y * kernelsL[l][3*72+5*9+6] + bc2.y * kernelsL[l][3*72+5*9+7] + br2.y * kernelsL[l][3*72+5*9+8] + 

        tl2.z * kernelsL[l][3*72+6*9+0] + tc2.z * kernelsL[l][3*72+6*9+1] + tr2.z * kernelsL[l][3*72+6*9+2] +
        ml2.z * kernelsL[l][3*72+6*9+3] + mc2.z * kernelsL[l][3*72+6*9+4] + mr2.z * kernelsL[l][3*72+6*9+5] +
        bl2.z * kernelsL[l][3*72+6*9+6] + bc2.z * kernelsL[l][3*72+6*9+7] + br2.z * kernelsL[l][3*72+6*9+8] + 

        tl2.w * kernelsL[l][3*72+7*9+0] + tc2.w * kernelsL[l][3*72+7*9+1] + tr2.w * kernelsL[l][3*72+7*9+2] +
        ml2.w * kernelsL[l][3*72+7*9+3] + mc2.w * kernelsL[l][3*72+7*9+4] + mr2.w * kernelsL[l][3*72+7*9+5] +
        bl2.w * kernelsL[l][3*72+7*9+6] + bc2.w * kernelsL[l][3*72+7*9+7] + br2.w * kernelsL[l][3*72+7*9+8] + biasL[l][3]
    ));
    float4 c5678 = RELU((float4)(
        tl1.x * kernelsL[l][4*72+0*9+0] + tc1.x * kernelsL[l][4*72+0*9+1] + tr1.x * kernelsL[l][4*72+0*9+2] +
        ml1.x * kernelsL[l][4*72+0*9+3] + mc1.x * kernelsL[l][4*72+0*9+4] + mr1.x * kernelsL[l][4*72+0*9+5] +
        bl1.x * kernelsL[l][4*72+0*9+6] + bc1.x * kernelsL[l][4*72+0*9+7] + br1.x * kernelsL[l][4*72+0*9+8] + 

        tl1.y * kernelsL[l][4*72+1*9+0] + tc1.y * kernelsL[l][4*72+1*9+1] + tr1.y * kernelsL[l][4*72+1*9+2] +
        ml1.y * kernelsL[l][4*72+1*9+3] + mc1.y * kernelsL[l][4*72+1*9+4] + mr1.y * kernelsL[l][4*72+1*9+5] +
        bl1.y * kernelsL[l][4*72+1*9+6] + bc1.y * kernelsL[l][4*72+1*9+7] + br1.y * kernelsL[l][4*72+1*9+8] + 

        tl1.z * kernelsL[l][4*72+2*9+0] + tc1.z * kernelsL[l][4*72+2*9+1] + tr1.z * kernelsL[l][4*72+2*9+2] +
        ml1.z * kernelsL[l][4*72+2*9+3] + mc1.z * kernelsL[l][4*72+2*9+4] + mr1.z * kernelsL[l][4*72+2*9+5] +
        bl1.z * kernelsL[l][4*72+2*9+6] + bc1.z * kernelsL[l][4*72+2*9+7] + br1.z * kernelsL[l][4*72+2*9+8] + 

        tl1.w * kernelsL[l][4*72+3*9+0] + tc1.w * kernelsL[l][4*72+3*9+1] + tr1.w * kernelsL[l][4*72+3*9+2] +
        ml1.w * kernelsL[l][4*72+3*9+3] + mc1.w * kernelsL[l][4*72+3*9+4] + mr1.w * kernelsL[l][4*72+3*9+5] +
        bl1.w * kernelsL[l][4*72+3*9+6] + bc1.w * kernelsL[l][4*72+3*9+7] + br1.w * kernelsL[l][4*72+3*9+8] +

        tl2.x * kernelsL[l][4*72+4*9+0] + tc2.x * kernelsL[l][4*72+4*9+1] + tr2.x * kernelsL[l][4*72+4*9+2] +
        ml2.x * kernelsL[l][4*72+4*9+3] + mc2.x * kernelsL[l][4*72+4*9+4] + mr2.x * kernelsL[l][4*72+4*9+5] +
        bl2.x * kernelsL[l][4*72+4*9+6] + bc2.x * kernelsL[l][4*72+4*9+7] + br2.x * kernelsL[l][4*72+4*9+8] + 

        tl2.y * kernelsL[l][4*72+5*9+0] + tc2.y * kernelsL[l][4*72+5*9+1] + tr2.y * kernelsL[l][4*72+5*9+2] +
        ml2.y * kernelsL[l][4*72+5*9+3] + mc2.y * kernelsL[l][4*72+5*9+4] + mr2.y * kernelsL[l][4*72+5*9+5] +
        bl2.y * kernelsL[l][4*72+5*9+6] + bc2.y * kernelsL[l][4*72+5*9+7] + br2.y * kernelsL[l][4*72+5*9+8] + 

        tl2.z * kernelsL[l][4*72+6*9+0] + tc2.z * kernelsL[l][4*72+6*9+1] + tr2.z * kernelsL[l][4*72+6*9+2] +
        ml2.z * kernelsL[l][4*72+6*9+3] + mc2.z * kernelsL[l][4*72+6*9+4] + mr2.z * kernelsL[l][4*72+6*9+5] +
        bl2.z * kernelsL[l][4*72+6*9+6] + bc2.z * kernelsL[l][4*72+6*9+7] + br2.z * kernelsL[l][4*72+6*9+8] + 

        tl2.w * kernelsL[l][4*72+7*9+0] + tc2.w * kernelsL[l][4*72+7*9+1] + tr2.w * kernelsL[l][4*72+7*9+2] +
        ml2.w * kernelsL[l][4*72+7*9+3] + mc2.w * kernelsL[l][4*72+7*9+4] + mr2.w * kernelsL[l][4*72+7*9+5] +
        bl2.w * kernelsL[l][4*72+7*9+6] + bc2.w * kernelsL[l][4*72+7*9+7] + br2.w * kernelsL[l][4*72+7*9+8] + biasL[l][4]
        ,
        tl1.x * kernelsL[l][5*72+0*9+0] + tc1.x * kernelsL[l][5*72+0*9+1] + tr1.x * kernelsL[l][5*72+0*9+2] +
        ml1.x * kernelsL[l][5*72+0*9+3] + mc1.x * kernelsL[l][5*72+0*9+4] + mr1.x * kernelsL[l][5*72+0*9+5] +
        bl1.x * kernelsL[l][5*72+0*9+6] + bc1.x * kernelsL[l][5*72+0*9+7] + br1.x * kernelsL[l][5*72+0*9+8] + 

        tl1.y * kernelsL[l][5*72+1*9+0] + tc1.y * kernelsL[l][5*72+1*9+1] + tr1.y * kernelsL[l][5*72+1*9+2] +
        ml1.y * kernelsL[l][5*72+1*9+3] + mc1.y * kernelsL[l][5*72+1*9+4] + mr1.y * kernelsL[l][5*72+1*9+5] +
        bl1.y * kernelsL[l][5*72+1*9+6] + bc1.y * kernelsL[l][5*72+1*9+7] + br1.y * kernelsL[l][5*72+1*9+8] + 

        tl1.z * kernelsL[l][5*72+2*9+0] + tc1.z * kernelsL[l][5*72+2*9+1] + tr1.z * kernelsL[l][5*72+2*9+2] +
        ml1.z * kernelsL[l][5*72+2*9+3] + mc1.z * kernelsL[l][5*72+2*9+4] + mr1.z * kernelsL[l][5*72+2*9+5] +
        bl1.z * kernelsL[l][5*72+2*9+6] + bc1.z * kernelsL[l][5*72+2*9+7] + br1.z * kernelsL[l][5*72+2*9+8] + 

        tl1.w * kernelsL[l][5*72+3*9+0] + tc1.w * kernelsL[l][5*72+3*9+1] + tr1.w * kernelsL[l][5*72+3*9+2] +
        ml1.w * kernelsL[l][5*72+3*9+3] + mc1.w * kernelsL[l][5*72+3*9+4] + mr1.w * kernelsL[l][5*72+3*9+5] +
        bl1.w * kernelsL[l][5*72+3*9+6] + bc1.w * kernelsL[l][5*72+3*9+7] + br1.w * kernelsL[l][5*72+3*9+8] +

        tl2.x * kernelsL[l][5*72+4*9+0] + tc2.x * kernelsL[l][5*72+4*9+1] + tr2.x * kernelsL[l][5*72+4*9+2] +
        ml2.x * kernelsL[l][5*72+4*9+3] + mc2.x * kernelsL[l][5*72+4*9+4] + mr2.x * kernelsL[l][5*72+4*9+5] +
        bl2.x * kernelsL[l][5*72+4*9+6] + bc2.x * kernelsL[l][5*72+4*9+7] + br2.x * kernelsL[l][5*72+4*9+8] + 

        tl2.y * kernelsL[l][5*72+5*9+0] + tc2.y * kernelsL[l][5*72+5*9+1] + tr2.y * kernelsL[l][5*72+5*9+2] +
        ml2.y * kernelsL[l][5*72+5*9+3] + mc2.y * kernelsL[l][5*72+5*9+4] + mr2.y * kernelsL[l][5*72+5*9+5] +
        bl2.y * kernelsL[l][5*72+5*9+6] + bc2.y * kernelsL[l][5*72+5*9+7] + br2.y * kernelsL[l][5*72+5*9+8] + 

        tl2.z * kernelsL[l][5*72+6*9+0] + tc2.z * kernelsL[l][5*72+6*9+1] + tr2.z * kernelsL[l][5*72+6*9+2] +
        ml2.z * kernelsL[l][5*72+6*9+3] + mc2.z * kernelsL[l][5*72+6*9+4] + mr2.z * kernelsL[l][5*72+6*9+5] +
        bl2.z * kernelsL[l][5*72+6*9+6] + bc2.z * kernelsL[l][5*72+6*9+7] + br2.z * kernelsL[l][5*72+6*9+8] + 

        tl2.w * kernelsL[l][5*72+7*9+0] + tc2.w * kernelsL[l][5*72+7*9+1] + tr2.w * kernelsL[l][5*72+7*9+2] +
        ml2.w * kernelsL[l][5*72+7*9+3] + mc2.w * kernelsL[l][5*72+7*9+4] + mr2.w * kernelsL[l][5*72+7*9+5] +
        bl2.w * kernelsL[l][5*72+7*9+6] + bc2.w * kernelsL[l][5*72+7*9+7] + br2.w * kernelsL[l][5*72+7*9+8] + biasL[l][5]
        ,
        tl1.x * kernelsL[l][6*72+0*9+0] + tc1.x * kernelsL[l][6*72+0*9+1] + tr1.x * kernelsL[l][6*72+0*9+2] +
        ml1.x * kernelsL[l][6*72+0*9+3] + mc1.x * kernelsL[l][6*72+0*9+4] + mr1.x * kernelsL[l][6*72+0*9+5] +
        bl1.x * kernelsL[l][6*72+0*9+6] + bc1.x * kernelsL[l][6*72+0*9+7] + br1.x * kernelsL[l][6*72+0*9+8] + 

        tl1.y * kernelsL[l][6*72+1*9+0] + tc1.y * kernelsL[l][6*72+1*9+1] + tr1.y * kernelsL[l][6*72+1*9+2] +
        ml1.y * kernelsL[l][6*72+1*9+3] + mc1.y * kernelsL[l][6*72+1*9+4] + mr1.y * kernelsL[l][6*72+1*9+5] +
        bl1.y * kernelsL[l][6*72+1*9+6] + bc1.y * kernelsL[l][6*72+1*9+7] + br1.y * kernelsL[l][6*72+1*9+8] + 

        tl1.z * kernelsL[l][6*72+2*9+0] + tc1.z * kernelsL[l][6*72+2*9+1] + tr1.z * kernelsL[l][6*72+2*9+2] +
        ml1.z * kernelsL[l][6*72+2*9+3] + mc1.z * kernelsL[l][6*72+2*9+4] + mr1.z * kernelsL[l][6*72+2*9+5] +
        bl1.z * kernelsL[l][6*72+2*9+6] + bc1.z * kernelsL[l][6*72+2*9+7] + br1.z * kernelsL[l][6*72+2*9+8] + 

        tl1.w * kernelsL[l][6*72+3*9+0] + tc1.w * kernelsL[l][6*72+3*9+1] + tr1.w * kernelsL[l][6*72+3*9+2] +
        ml1.w * kernelsL[l][6*72+3*9+3] + mc1.w * kernelsL[l][6*72+3*9+4] + mr1.w * kernelsL[l][6*72+3*9+5] +
        bl1.w * kernelsL[l][6*72+3*9+6] + bc1.w * kernelsL[l][6*72+3*9+7] + br1.w * kernelsL[l][6*72+3*9+8] +

        tl2.x * kernelsL[l][6*72+4*9+0] + tc2.x * kernelsL[l][6*72+4*9+1] + tr2.x * kernelsL[l][6*72+4*9+2] +
        ml2.x * kernelsL[l][6*72+4*9+3] + mc2.x * kernelsL[l][6*72+4*9+4] + mr2.x * kernelsL[l][6*72+4*9+5] +
        bl2.x * kernelsL[l][6*72+4*9+6] + bc2.x * kernelsL[l][6*72+4*9+7] + br2.x * kernelsL[l][6*72+4*9+8] + 

        tl2.y * kernelsL[l][6*72+5*9+0] + tc2.y * kernelsL[l][6*72+5*9+1] + tr2.y * kernelsL[l][6*72+5*9+2] +
        ml2.y * kernelsL[l][6*72+5*9+3] + mc2.y * kernelsL[l][6*72+5*9+4] + mr2.y * kernelsL[l][6*72+5*9+5] +
        bl2.y * kernelsL[l][6*72+5*9+6] + bc2.y * kernelsL[l][6*72+5*9+7] + br2.y * kernelsL[l][6*72+5*9+8] + 

        tl2.z * kernelsL[l][6*72+6*9+0] + tc2.z * kernelsL[l][6*72+6*9+1] + tr2.z * kernelsL[l][6*72+6*9+2] +
        ml2.z * kernelsL[l][6*72+6*9+3] + mc2.z * kernelsL[l][6*72+6*9+4] + mr2.z * kernelsL[l][6*72+6*9+5] +
        bl2.z * kernelsL[l][6*72+6*9+6] + bc2.z * kernelsL[l][6*72+6*9+7] + br2.z * kernelsL[l][6*72+6*9+8] + 

        tl2.w * kernelsL[l][6*72+7*9+0] + tc2.w * kernelsL[l][6*72+7*9+1] + tr2.w * kernelsL[l][6*72+7*9+2] +
        ml2.w * kernelsL[l][6*72+7*9+3] + mc2.w * kernelsL[l][6*72+7*9+4] + mr2.w * kernelsL[l][6*72+7*9+5] +
        bl2.w * kernelsL[l][6*72+7*9+6] + bc2.w * kernelsL[l][6*72+7*9+7] + br2.w * kernelsL[l][6*72+7*9+8] + biasL[l][6]
        ,
        tl1.x * kernelsL[l][7*72+0*9+0] + tc1.x * kernelsL[l][7*72+0*9+1] + tr1.x * kernelsL[l][7*72+0*9+2] +
        ml1.x * kernelsL[l][7*72+0*9+3] + mc1.x * kernelsL[l][7*72+0*9+4] + mr1.x * kernelsL[l][7*72+0*9+5] +
        bl1.x * kernelsL[l][7*72+0*9+6] + bc1.x * kernelsL[l][7*72+0*9+7] + br1.x * kernelsL[l][7*72+0*9+8] + 

        tl1.y * kernelsL[l][7*72+1*9+0] + tc1.y * kernelsL[l][7*72+1*9+1] + tr1.y * kernelsL[l][7*72+1*9+2] +
        ml1.y * kernelsL[l][7*72+1*9+3] + mc1.y * kernelsL[l][7*72+1*9+4] + mr1.y * kernelsL[l][7*72+1*9+5] +
        bl1.y * kernelsL[l][7*72+1*9+6] + bc1.y * kernelsL[l][7*72+1*9+7] + br1.y * kernelsL[l][7*72+1*9+8] + 

        tl1.z * kernelsL[l][7*72+2*9+0] + tc1.z * kernelsL[l][7*72+2*9+1] + tr1.z * kernelsL[l][7*72+2*9+2] +
        ml1.z * kernelsL[l][7*72+2*9+3] + mc1.z * kernelsL[l][7*72+2*9+4] + mr1.z * kernelsL[l][7*72+2*9+5] +
        bl1.z * kernelsL[l][7*72+2*9+6] + bc1.z * kernelsL[l][7*72+2*9+7] + br1.z * kernelsL[l][7*72+2*9+8] + 

        tl1.w * kernelsL[l][7*72+3*9+0] + tc1.w * kernelsL[l][7*72+3*9+1] + tr1.w * kernelsL[l][7*72+3*9+2] +
        ml1.w * kernelsL[l][7*72+3*9+3] + mc1.w * kernelsL[l][7*72+3*9+4] + mr1.w * kernelsL[l][7*72+3*9+5] +
        bl1.w * kernelsL[l][7*72+3*9+6] + bc1.w * kernelsL[l][7*72+3*9+7] + br1.w * kernelsL[l][7*72+3*9+8] +

        tl2.x * kernelsL[l][7*72+4*9+0] + tc2.x * kernelsL[l][7*72+4*9+1] + tr2.x * kernelsL[l][7*72+4*9+2] +
        ml2.x * kernelsL[l][7*72+4*9+3] + mc2.x * kernelsL[l][7*72+4*9+4] + mr2.x * kernelsL[l][7*72+4*9+5] +
        bl2.x * kernelsL[l][7*72+4*9+6] + bc2.x * kernelsL[l][7*72+4*9+7] + br2.x * kernelsL[l][7*72+4*9+8] + 

        tl2.y * kernelsL[l][7*72+5*9+0] + tc2.y * kernelsL[l][7*72+5*9+1] + tr2.y * kernelsL[l][7*72+5*9+2] +
        ml2.y * kernelsL[l][7*72+5*9+3] + mc2.y * kernelsL[l][7*72+5*9+4] + mr2.y * kernelsL[l][7*72+5*9+5] +
        bl2.y * kernelsL[l][7*72+5*9+6] + bc2.y * kernelsL[l][7*72+5*9+7] + br2.y * kernelsL[l][7*72+5*9+8] + 

        tl2.z * kernelsL[l][7*72+6*9+0] + tc2.z * kernelsL[l][7*72+6*9+1] + tr2.z * kernelsL[l][7*72+6*9+2] +
        ml2.z * kernelsL[l][7*72+6*9+3] + mc2.z * kernelsL[l][7*72+6*9+4] + mr2.z * kernelsL[l][7*72+6*9+5] +
        bl2.z * kernelsL[l][7*72+6*9+6] + bc2.z * kernelsL[l][7*72+6*9+7] + br2.z * kernelsL[l][7*72+6*9+8] + 

        tl2.w * kernelsL[l][7*72+7*9+0] + tc2.w * kernelsL[l][7*72+7*9+1] + tr2.w * kernelsL[l][7*72+7*9+2] +
        ml2.w * kernelsL[l][7*72+7*9+3] + mc2.w * kernelsL[l][7*72+7*9+4] + mr2.w * kernelsL[l][7*72+7*9+5] +
        bl2.w * kernelsL[l][7*72+7*9+6] + bc2.w * kernelsL[l][7*72+7*9+7] + br2.w * kernelsL[l][7*72+7*9+8] + biasL[l][7]
    ));

    write_imagef(tmpImgOut, coord1, c1234);
    write_imagef(tmpImgOut, coord2, c5678);
}

__kernel void convTranspose8To1(
    __read_only image2d_array_t tmpImgIn,
    __write_only image2d_t dstImg)
{
    const int x = get_global_id(0), y = get_global_id(1);
    if(x >= get_image_width(dstImg) || y >= get_image_height(dstImg))
        return;

    int2 coord = (int2)(x, y);
    int2 orgCoord = coord / 2;

    int2 pos = coord & 1;
    int index = pos.y * 2 + pos.x;

    float4 mc1 = read_imagef(tmpImgIn, samplerN, (int4)(orgCoord, 0, 0));
    float4 mc2 = read_imagef(tmpImgIn, samplerN, (int4)(orgCoord, 1, 0));

    float c = clamp(
        mc1.x * kernelsL10[0 + index] +
        mc1.y * kernelsL10[4 + index] +
        mc1.z * kernelsL10[8 + index] +
        mc1.w * kernelsL10[12 + index] +
        mc2.x * kernelsL10[16 + index] +
        mc2.y * kernelsL10[20 + index] +
        mc2.z * kernelsL10[24 + index] +
        mc2.w * kernelsL10[28 + index], 0.0f, 1.0f);

    write_imagef(dstImg, coord, (float4)(c, 0.0f, 0.0f, 1.0f));
}
